code: def collate_fn(self, batch):
      input_ids = [item["input_ids"][:-1].tolist() for item in batch]
      labels = [item["input_ids"][1:].tolist() for item in batch]
      contexts = [item["context_ids"].tolist() for item in batch]
      answers = [item["answer_ids"].tolist() for item in batch]
      loss_mask = [item["mask"][1:].tolist() for item in batch]
      metadata = [item["metadata"] for item in batch]
  
      max_length = max(
          max([len(x) for x in input_ids]),
          max([len(x) for x in contexts]) + self.tokens_to_generate,
      )
      if max_length > self.max_seq_length:
          # truncate the sequences if it is longer than max_seq_length
          input_ids = [x[: self.max_seq_length] for x in input_ids]
          labels = [x[: self.max_seq_length] for x in labels]
          loss_mask = [x[: self.max_seq_length] for x in loss_mask]
          contexts = [x[: self.max_seq_length] for x in contexts]
          answers = [x[: self.max_seq_length] for x in answers]
  
      # increase max length to nearest multiple of 4 or 8
      if self.pad_to_max_length:
          max_length = self.max_seq_length
      else:
          max_length = min(
              self.max_seq_length, self._ceil_to_nearest(max_length, 8)
          )
      assert max_length <= self.max_seq_length
  
      attention_mask = [
          self._create_attention_mask(max_length) for _ in batch
      ]
      attention_mask = torch.stack(attention_mask)
      position_ids = [list(range(max_length)) for _ in batch]
      position_ids = torch.LongTensor(position_ids)
      input_ids = torch.LongTensor(
          self._collate_item(
              input_ids,
              max_length=max_length,
              pad_id=self.tokenizer.eos_id,
          )
      )
      labels = torch.LongTensor(
          self._collate_item(
              labels,
              max_length=max_length,
              pad_id=self.tokenizer.eos_id,
          )
      )
      loss_mask = torch.LongTensor(
          self._collate_item(loss_mask, max_length=max_length, pad_id=0)
      )
      context_lengths = torch.LongTensor([len(x) for x in contexts])
      contexts = torch.LongTensor(
          self._collate_item(
              contexts,
              max_length=max_length,
              pad_id=self.tokenizer.eos_id,
          )
      )
      answers = torch.LongTensor(
          self._collate_item(
              answers,
              max_length=max_length,
              pad_id=self.tokenizer.eos_id,
          )
      )
  
      processed_batch = {
          "tokens": input_ids,
          "labels": labels,
          "attention_mask": attention_mask,
          "loss_mask": loss_mask,
          "position_ids": position_ids,
          "contexts": contexts,
          "context_lengths": context_lengths,
          "answers": answers,
          "metadata": metadata,
      }
  
      return processed_batch
  
file_path: NeMo/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py
imports: import copy
  import torch
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.language_modeling.megatron.gpt_sft_dataset import GPTSFTDataset
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the GPTSFTChatDataset obj's underlying member method code with example usage: obj.collate_fn(batch) and returns: processed_batch